Any Source → Any Target
Database Migration Services: Any Source, Any Target, Minimal Downtime
Database migration services provide end-to-end planning, schema conversion, data synchronization, and low-downtime cutover across cloud and on-premises environments. JusDB Database SREs execute heterogeneous migrations (Oracle/SQL Server to PostgreSQL/MySQL) and engine upgrades using Change Data Capture (CDC) pipelines with automated row-level validation and a tested rollback plan agreed in the SOW. JusDB's security practices are aligned with ISO 27001 and SOC 2 Type II, and JusDB is pursuing both certifications (see Terms, section 12).
JusDB migrates databases across every pattern — Oracle to PostgreSQL, MySQL to TiDB, on-premises to AWS/GCP/Azure, version upgrades, and cloud-to-cloud exits. We use the full migration toolkit (Debezium, gh-ost, pglogical, AWS DMS) with CDC-based live replication, parallel run validation, and a tested rollback plan before we start.
Migrating a specific engine? Oracle→PostgreSQL → MySQL migrations → MySQL→TiDB →
Migration Patterns We Handle
Every migration pattern has a different risk profile, toolchain, and cutover strategy. JusDB has executed all four — including the highest-risk heterogeneous engine replacements.
On-Premises → Cloud
Risk: Low- → Oracle on bare metal
- → MySQL on VMware
- → SQL Server on Windows Server
- → PostgreSQL on Linux
- AWS RDS / Aurora
- GCP Cloud SQL
- Azure Database
- Self-managed on EC2/GCE
Network-level replication via DMS or Debezium CDC, live cutover in a short, agreed off-peak window.
Engine Replacement (Heterogeneous)
Risk: High- → Oracle → PostgreSQL
- → MySQL → PostgreSQL
- → SQL Server → PostgreSQL
- → MySQL → TiDB
- PostgreSQL (any cloud)
- TiDB (horizontal scale)
- MariaDB
- Aurora-compatible
Schema conversion (pgloader, AWS SCT, Ora2pg), stored procedure rewrite, application query adaptation, parallel run validation.
Version Upgrade
Risk: Medium- → MySQL 5.7 → 8.0
- → PostgreSQL 12 → 16
- → MongoDB 4.x → 6.x
- → Cassandra 3 → 4
- Latest stable version
- Cloud-managed version
Blue-green upgrade with parallel testing, gh-ost or pg_upgrade, incremental replica promotion. Rollback-safe.
Cloud → Cloud / Cloud Exits
Risk: Low–Medium- → RDS → self-managed EC2
- → Azure SQL → AWS RDS
- → Atlas → self-hosted Mongo
- Any cloud or on-prem target
CDC replication maintains sync during migration window; traffic shifted via connection string swap or proxy.
Critical Failure Modes We Actively Protect Against
Database migrations are unforgiving. Standard tutorials miss the edge cases that trigger midnight rollbacks or data corruption. Here is how we mitigate them:
CDC Replication Slot Disk Saturation & WAL Accumulation Panic
During long-running initial bulk loads, source database replication slots hold WAL/binlogs indefinitely if the consumer pauses. Disk volume fills to 100%, causing the primary database to crash and refuse restart.
We configure max_slot_wal_keep_size thresholds, enforce automated WAL spool monitoring, and stage partitioned snapshot backfills independently of active transaction streaming.
Heterogeneous Type Truncation & Silent Precision Loss
Translating numeric, timestamp with timezone, or multibyte UTF-8 string types between engines (e.g. Oracle NUMBER(38) to PostgreSQL) silently truncates decimal precision or corrupts unicode characters.
We run automated column-level schema mapping linters and deploy shadow validation scripts that compare cryptographic hashes of primary key rows across both source and target.
Cutover Dual-Write Split-Brain & Unsynchronized Sequences
During cutover, application traffic writes to both old and new databases simultaneously, or target auto-increment sequences fail to synchronize, resulting in fatal duplicate key collisions on insert.
We enforce transactional read-only locks on source tables, execute sequence synchronization scripts, and maintain reverse-CDC pipelines so traffic can fail back to the source if needed.
Our engineers run automated telemetry queries to verify replication lag, WAL accumulation, and replica synchronization before authorizing cutover:
SELECT slot_name,
plugin,
active,
pg_size_pretty(
pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn)
) AS wal_retained_bytes,
round(
pg_wal_lsn_diff(pg_current_wal_lsn(), confirmed_flush_lsn) / 1024 / 1024, 2
) AS lag_mb
FROM pg_replication_slots;SELECT channel_name,
service_state,
last_error_number,
last_error_message
FROM performance_schema.replication_applier_status_by_worker
WHERE last_error_number > 0;
-- Seconds behind source check
SHOW REPLICA STATUS\GThe JusDB Migration Toolkit
One-size-fits-all migration tools do not exist. JusDB selects the right tool combination for your source engine, target engine, data volume, and downtime budget.
CDC & Replication
Open-source CDC platform — captures row-level changes from MySQL, PostgreSQL, MongoDB, Oracle binlogs/WAL/oplog and streams to Kafka.
Managed migration service for heterogeneous migrations (Oracle→PG, MySQL→Aurora). Good for bulk load + CDC, limited for complex schema transforms.
Logical replication extension for PostgreSQL — enables cross-version PG replication and live cutover without pg_upgrade downtime.
Lightweight MySQL binlog readers — simpler than Debezium for MySQL-only CDC pipelines.
Schema Migration & Conversion
Online schema changes for MySQL: ALTER TABLE without long table locks, with only a brief lock at cut-over. A common choice for very large MySQL tables.
Bulk data loading and schema conversion for MySQL→PostgreSQL, SQLite→PostgreSQL, CSV imports.
Oracle to PostgreSQL migration toolkit — converts schema, stored procedures (PL/SQL→PL/pgSQL), and data.
Automated schema conversion for Oracle→RDS, SQL Server→Aurora. Good starting point; always requires manual refinement.
Validation & Testing
Verifies row-level data consistency between MySQL source and target — runs without locking tables.
Schema and data comparison between PostgreSQL databases for migration validation.
Open-source table-level diff tool — works across MySQL, PostgreSQL, BigQuery, Snowflake.
JusDB replays production query load against the target database before cutover to validate performance parity.
Migration Risk Management
Every migration has risks. JusDB's risk framework addresses each one explicitly — before the migration starts, not during it.
| Risk | JusDB Mitigation |
|---|---|
Data Loss | CDC replication with lag monitoring; dual-write period before cutover; checksum validation post-migration. |
Extended Downtime | Low-downtime migrations using CDC and an atomic cutover in a short, planned window. |
Application Compatibility | SQL dialect analysis before migration; query rewrite catalogue; parallel run period with application traffic split. |
Performance Regression | Load test on target with production query sample before cutover; query plan comparison; index audit on target. |
Rollback Failure | JusDB defines and tests a rollback plan before the migration starts — including reverse replication from target back to source. |
How JusDB Database Migration compares to alternative migration models.
Database migrations carry existential business risks if data desynchronizes or cutovers stall. Here is how JusDB's low-downtime database migration engineering contrasts with cloud-native tools, Big-4 consultancies, and internal custom scripting.
| Migration Dimension | JusDB Low-Downtime Migration | Cloud Tools (AWS DMS) | Big-4 / Generic IT | In-House Scripts |
|---|---|---|---|---|
| Low-Downtime Replication & CDC Architecture | Production-grade Change Data Capture (Debezium, Kafka, pglogical, GTID) designed for sub-second lag without write locks | Basic DMS tools often stall on high write volume, causing unrecoverable CDC synchronization loops | Relies on legacy scheduled maintenance windows with extended multi-hour production downtime | Custom batch export/import scripts causing severe application downtime and missing transaction writes |
| Heterogeneous Schema & Procedure Conversion | Deep automated translation of proprietary DDL, stored procedures, triggers, and sequences (Oracle/MSSQL to PostgreSQL) | Basic schema conversion tools (SCT) flag errors without generating complete production-ready SQL rewrites | Manual, slow manual code translation with high hourly billing rates and prolonged delivery timelines | Manual developer conversion prone to subtle data type mismatches, silent truncation, and timezone bugs |
| Data Consistency & Automated Verification | Automated row-level checksum verification, primary key hash auditing, and concurrent shadow traffic testing | Limited validation (table row counts only); misses subtle column truncation or character encoding errors | Ad-hoc spot checking leaving critical data anomalies undiscovered until post-cutover user complaints | Spot-checks with manual SELECT COUNT(*) queries; blind to column-level data divergence |
| Rollback Preparedness & Fallback Safety | Reverse-CDC synchronization where the engines support it, so traffic can fail back to the source; rollback terms are defined in the SOW | One-way sync; once cutover occurs, falling back requires hours of manual data extraction and downtime | Rollback often means restoring an earlier backup, losing transactions written after it was taken | High panic risk; cutovers are treated as one-way 'point of no return' events |
| Security, Encryption & Ephemeral Access | Ephemeral, session-logged bastion tunnels with end-to-end TLS 1.3 encryption and no permanent credentials | Requires broad Cloud IAM administrator permissions and persistent cross-account VPC peering | Static master credentials shared across overseas contracting teams with unmonitored access | Direct developer SSH access with unencrypted transient intermediate dump files on local disks |
| Live Cutover Window & Operational Ownership | Cutover window planned and rehearsed per workload, with senior Database SREs managing traffic rerouting live in your war room | Self-serve tooling; zero operational assistance or incident triage during cutover | Off-hours operations managed by rotating contractors with escalation delays during unexpected errors | High stress, midnight cutovers plagued by unexpected connection timeouts and DNS propagation delays |
How a JusDB Migration Works
Discovery
Inventory source schema, row counts, data types, stored procedures, and application queries. Identify incompatibilities before a single row moves.
Design
Schema conversion plan, CDC pipeline design, cutover window design, rollback plan. Written runbook before work starts.
Migrate & Validate
Bulk data load + CDC lag catch-up. Parallel run period: validate data integrity (checksums), query correctness, and performance on target.
Cutover
Atomic cutover in a short, planned window. Application connection string swap. Source kept in read-only sync for rollback window (typically 48–72 hours).
FAQ
Plan your migration before you start it
JusDB audits your source database, designs the migration architecture, builds the rollback plan, and executes the migration — any source, any target, any cloud.